Special Issue "Cyber-Physical Systems: Data Processing and Communication Architectures"
Deadline for manuscript submissions: 15 August 2018
Last generation computing architectures have evolved from traditional stand-alone embedded systems to complex environments, where computational elements tightly interact with physical entities, such as sensors networks and I/O devices. These systems, usually referred as cyber-physical Systems (CPS), enabled a flourishing ecosystem of architectures and platforms where smart objects, users and communication infrastructures interact to support intelligent context-aware services and applications. Smart grids, medical monitoring, smart cities, distributed pollution and tracking are just a few examples of concrete applications that are gaining attraction among industries and institutions. However, the mobility and pervasiveness requirements of such environments impose energy consumption constraints that must be met in a context of increasing computational needs, due the processing of large amounts of data coming from sensing and input devices.
This Special Issue aims at exploring emerging approaches, ideas and contributions to address the challenges in the design of energy efficient computational-centric smart objects in CPS. Potential topics include, but are not limited to:
- Design Platforms and Tools for IoT-based ecosystems for optimizing energy/performance tradeoffs
- Deep Learning and Deep Computation for CPS
- Novel architectures for embedded low power computing in CPS
- Approximate Computing for energy-efficient applications
- Network-on-Chip Architectures
- Wireless Sensor Networks
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Cyber-Physical Systems
- Deep Learning
- Approximate Computing
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Procedural abduction as an ampliative reasoning and decisionmaking mechanism for smart cyber-physical systems
Author: Imre Horváth
Affiliation: Cyber-Physical Systems Design, Industrial Design Engineering, Delft University of Technology, The Netherlands
Abstract: Though artificial intelligence, artificial generic intelligence, machine learning, and autonomous mental development have produced remarkable results in the last two decades, the issue of implementing system-level multi-actor intelligence (SL-MAI) in cyber-physical systems (CPSs) is still an open issue. Recent efforts were made to explore the affordances of collaborating multiagent (CMA) technologies and networked deep learning (NDL) technologies in dominantly software systems. Procedural abduction (PA) is proposed here as a hybrid deep computation and deep learning mechanism for self-managing knowledge-intensive system, which cannot be implemented without this, or similar assets. Actually, PA has been conceptualized as an ampliative reasoning and decision-making mechanism for smart CPSs (S-CPSs). It has a robust theoretical foundation and logical framework, as well as an efficient computational procedure, which allows its exploitation in many applications. PA includes eight clusters of activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness concerning the performance of the system at attaining its operation/servicing objectives, (v) devising alternative performance enhancement strategies, (vi) designing adaptation of the parts and the system as a whole, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. As a computational approach, PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs with regards to the most relevant objective of servicing and ‘best option’-based realization of their demanded adaptation. Computational realization of PA necessitates a combination (compositional fusion) of a large number of conventional and specific artificial intelligence algorithms. A fully fledged implementation of PA is underway, which will make verification and validation in the context of various smart CPSs possible.
Title: Solving the Job Shop Scheduling Problem in the Industry 4.0 Era
Authors: Matheus E. Leusin¹, Enzo M. Frazzon¹, Mauricio U. Maldonado¹, Mirko Kück² and Michael Freitag²
Affiliation 1: Federal University of Santa Catarina, Florianópolis, Brazil
Affiliation 2: University of Bremen, Bremen, Germany
Abstract: Technological development along with the emergence of Industry 4.0 new concepts allows for the resolution of typical industrial dilemmas, such as the classic Job Shop Scheduling Problem (JSP). The embedding of Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) represents a highly promising combination to handle JSP's complexity and dynamics. In this sense, this paper proposes a data exchange framework to deal with the JSP, considering the state-of-the-art knowledge regarding MAS and CPS, as well as current industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line, made possible through the development of an intelligent system based on the use of agents and Internet of Things (IoT) to achieve real-time data exchange and decision making in the job shop. Moreover, the performance of the proposed framework is tested through a simulation model based on a real industrial case. The results substantiated gains in flexibility, scalability, and efficiency through the data exchange integration between factory layers. Finally, insights are presented regarding industrial applications in the Industry 4.0 era in general, and in particular with regard to the framework implementation in the analyzed industrial case.
Keywords: Multi-Agent Systems; Internet of Things, IoT, Digital Manufacturing; Job Shop Scheduling Problem.
Title: Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems
Authors: Kyoung-Don Kang and Ankur Vora
Affiliation: Department of Computer Science, State University of New York at Binghamton
Abstract: Emerging 5G wireless communication technology is envisioned to significantly enhance the real-time communication in cyber-physical systems. In this paper, we propose a new algorithm for effective cross-layer downlink scheduling and resource allocation (SRA) considering the channel and queue state, while supporting fairness. We also integrate our cross-layer SRA scheme with filter-bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) to leverage the higher spectral efficiency. Our performance evaluation results show that our SRA method outperforms a novel SRA algorithm by up to approximately 60%, 2.6%, and 1.6% in terms of goodput, goodput fairness, and delay fairness, respectively.
Title: Model-Based Software Mapping for Energy Efficient Sensor-Based Human Activity Recognition
Authors: Florian Grützmacher, Albert Hein, Thomas Kirste, and Christian Haubelt
Affiliation: University of Rostock, Germany
Abstract: The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for Human Activity Recognition. Multiple such sensors attached to the human body for gathering, processing and transmitting sensor data connected to a platforms for classification form a heterogeneous distributed Cyber-Physical System. Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed hardware. However, the software mapping is decisive for the wearable's processing load and the amount of data which has to be transmitted wirelessly, influencing it's energy consumption to a great extent. Thus, the software mapping significantly influences the energy consumption of the wearable. An energy-efficient design of the system is crucial to prolong battery lifetimes and allow long-term usage of the system. In order to substantiate early design decisions, energy consumption estimations of different mappings are necessary at design time of the system. We propose to combine well known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of different software mappings.
Title: Applying Semantics to Reduce the Time to Analytics in Industrial Settings
Authors: Tobias Meisen, André Pomp and Christian Kohlschein
Affiliation: Faculty for Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany
Abstract: In today's age of Cyber Physical (Production) Systems, large amounts of heterogeneous data are generated in industrial settings every second. Enabling data aggregation and analytics, many companies currently focus on the approach of data lakes. While this provides centralized storage of all available kinds of data, new challenges arise as the stored data has to be found, understood and processed. To reduce the time to analytics for data scientists, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline collects data from machines on the shop floor and connected systems and feeds it into ESKAPE, where a semantic data integration is performed. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world use case.